Overview

Dataset statistics

Number of variables20
Number of observations4484
Missing cells11067
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory700.8 KiB
Average record size in memory160.0 B

Variable types

Numeric13
Categorical5
Text2

Alerts

Area Code (M49) is highly overall correlated with AreaHigh correlation
Element Code is highly overall correlated with Element and 3 other fieldsHigh correlation
2010 is highly overall correlated with 2011 and 12 other fieldsHigh correlation
2011 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2012 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2013 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2014 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2015 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2016 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2017 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2018 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2019 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
2020 is highly overall correlated with 2010 and 12 other fieldsHigh correlation
Area is highly overall correlated with Area Code (M49)High correlation
Element is highly overall correlated with Element Code and 3 other fieldsHigh correlation
Unit is highly overall correlated with Element Code and 14 other fieldsHigh correlation
Flag is highly overall correlated with Element Code and 14 other fieldsHigh correlation
Flag Description is highly overall correlated with Element Code and 14 other fieldsHigh correlation
Unit is highly imbalanced (61.8%)Imbalance
Flag is highly imbalanced (54.8%)Imbalance
Flag Description is highly imbalanced (54.8%)Imbalance
2010 has 1366 (30.5%) missing valuesMissing
2011 has 1367 (30.5%) missing valuesMissing
2012 has 1367 (30.5%) missing valuesMissing
2013 has 1366 (30.5%) missing valuesMissing
2014 has 950 (21.2%) missing valuesMissing
2015 has 955 (21.3%) missing valuesMissing
2016 has 943 (21.0%) missing valuesMissing
2017 has 939 (20.9%) missing valuesMissing
2018 has 923 (20.6%) missing valuesMissing
2019 has 418 (9.3%) missing valuesMissing
2020 has 473 (10.5%) missing valuesMissing
2010 is highly skewed (γ1 = 22.0968211)Skewed
2011 is highly skewed (γ1 = 22.14911813)Skewed
2012 is highly skewed (γ1 = 22.25017621)Skewed
2013 is highly skewed (γ1 = 22.30139668)Skewed
2014 is highly skewed (γ1 = 23.56759727)Skewed
2015 is highly skewed (γ1 = 23.66687661)Skewed
2016 is highly skewed (γ1 = 23.97286776)Skewed
2017 is highly skewed (γ1 = 23.99179968)Skewed
2018 is highly skewed (γ1 = 23.80911335)Skewed
2019 is highly skewed (γ1 = 25.18254548)Skewed
2020 is highly skewed (γ1 = 24.97640502)Skewed
2010 has 1403 (31.3%) zerosZeros
2011 has 1371 (30.6%) zerosZeros
2012 has 1348 (30.1%) zerosZeros
2013 has 1349 (30.1%) zerosZeros
2014 has 1522 (33.9%) zerosZeros
2015 has 1531 (34.1%) zerosZeros
2016 has 1530 (34.1%) zerosZeros
2017 has 1533 (34.2%) zerosZeros
2018 has 1536 (34.3%) zerosZeros
2019 has 1813 (40.4%) zerosZeros
2020 has 1789 (39.9%) zerosZeros

Reproduction

Analysis started2023-07-16 13:03:07.425847
Analysis finished2023-07-16 13:04:14.906495
Duration1 minute and 7.48 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Area Code (M49)
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean534.68287
Minimum108
Maximum834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2023-07-16T16:04:15.133495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile108
Q1180
median646
Q3800
95-th percentile834
Maximum834
Range726
Interquartile range (IQR)620

Descriptive statistics

Standard deviation273.99112
Coefficient of variation (CV)0.51243669
Kurtosis-1.4431467
Mean534.68287
Median Absolute Deviation (MAD)188
Skewness-0.42258157
Sum2397518
Variance75071.135
MonotonicityIncreasing
2023-07-16T16:04:15.386496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
834 699
15.6%
404 693
15.5%
800 676
15.1%
646 652
14.5%
180 648
14.5%
108 575
12.8%
728 541
12.1%
ValueCountFrequency (%)
108 575
12.8%
180 648
14.5%
404 693
15.5%
646 652
14.5%
728 541
12.1%
800 676
15.1%
834 699
15.6%
ValueCountFrequency (%)
834 699
15.6%
800 676
15.1%
728 541
12.1%
646 652
14.5%
404 693
15.5%
180 648
14.5%
108 575
12.8%

Area
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
United Republic of Tanzania
699 
Kenya
693 
Uganda
676 
Rwanda
652 
Democratic Republic of the Congo
648 
Other values (2)
1116 

Length

Max length32
Median length11
Mean length13.607939
Min length5

Characters and Unicode

Total characters61018
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBurundi
2nd rowBurundi
3rd rowBurundi
4th rowBurundi
5th rowBurundi

Common Values

ValueCountFrequency (%)
United Republic of Tanzania 699
15.6%
Kenya 693
15.5%
Uganda 676
15.1%
Rwanda 652
14.5%
Democratic Republic of the Congo 648
14.5%
Burundi 575
12.8%
South Sudan 541
12.1%

Length

2023-07-16T16:04:15.893498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:04:16.626495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
republic 1347
13.9%
of 1347
13.9%
united 699
7.2%
tanzania 699
7.2%
kenya 693
7.1%
uganda 676
7.0%
rwanda 652
 
6.7%
democratic 648
 
6.7%
the 648
 
6.7%
congo 648
 
6.7%
Other values (3) 1657
17.1%

Most occurring characters

ValueCountFrequency (%)
a 6635
 
10.9%
n 5882
 
9.6%
5230
 
8.6%
e 4035
 
6.6%
i 3968
 
6.5%
o 3832
 
6.3%
u 3579
 
5.9%
d 3143
 
5.2%
c 2643
 
4.3%
t 2536
 
4.2%
Other values (19) 19535
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48069
78.8%
Uppercase Letter 7719
 
12.7%
Space Separator 5230
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6635
13.8%
n 5882
12.2%
e 4035
 
8.4%
i 3968
 
8.3%
o 3832
 
8.0%
u 3579
 
7.4%
d 3143
 
6.5%
c 2643
 
5.5%
t 2536
 
5.3%
f 1347
 
2.8%
Other values (10) 10469
21.8%
Uppercase Letter
ValueCountFrequency (%)
R 1999
25.9%
U 1375
17.8%
S 1082
14.0%
T 699
 
9.1%
K 693
 
9.0%
D 648
 
8.4%
C 648
 
8.4%
B 575
 
7.4%
Space Separator
ValueCountFrequency (%)
5230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55788
91.4%
Common 5230
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6635
 
11.9%
n 5882
 
10.5%
e 4035
 
7.2%
i 3968
 
7.1%
o 3832
 
6.9%
u 3579
 
6.4%
d 3143
 
5.6%
c 2643
 
4.7%
t 2536
 
4.5%
R 1999
 
3.6%
Other values (18) 17536
31.4%
Common
ValueCountFrequency (%)
5230
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6635
 
10.9%
n 5882
 
9.6%
5230
 
8.6%
e 4035
 
6.6%
i 3968
 
6.5%
o 3832
 
6.3%
u 3579
 
5.9%
d 3143
 
5.2%
c 2643
 
4.3%
t 2536
 
4.2%
Other values (19) 19535
32.0%

Element Code
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4750.0544
Minimum511
Maximum5911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2023-07-16T16:04:17.449497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum511
5-th percentile645
Q15142
median5301
Q35611
95-th percentile5911
Maximum5911
Range5400
Interquartile range (IQR)469

Descriptive statistics

Standard deviation1697.2583
Coefficient of variation (CV)0.35731345
Kurtosis1.9605566
Mean4750.0544
Median Absolute Deviation (MAD)210
Skewness-1.9447228
Sum21299244
Variance2880685.9
MonotonicityNot monotonic
2023-07-16T16:04:17.892496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5611 724
16.1%
5142 705
15.7%
5301 673
15.0%
645 635
14.2%
5911 612
13.6%
5170 595
13.3%
5511 533
11.9%
511 7
 
0.2%
ValueCountFrequency (%)
511 7
 
0.2%
645 635
14.2%
5142 705
15.7%
5170 595
13.3%
5301 673
15.0%
5511 533
11.9%
5611 724
16.1%
5911 612
13.6%
ValueCountFrequency (%)
5911 612
13.6%
5611 724
16.1%
5511 533
11.9%
5301 673
15.0%
5170 595
13.3%
5142 705
15.7%
645 635
14.2%
511 7
 
0.2%

Element
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
Import Quantity
724 
Food
705 
Domestic supply quantity
673 
Food supply quantity (kg/capita/yr)
635 
Export Quantity
612 
Other values (3)
1135 

Length

Max length35
Median length24
Mean length16.084969
Min length4

Characters and Unicode

Total characters72125
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTotal Population - Both sexes
2nd rowFood supply quantity (kg/capita/yr)
3rd rowFood supply quantity (kg/capita/yr)
4th rowFood supply quantity (kg/capita/yr)
5th rowFood supply quantity (kg/capita/yr)

Common Values

ValueCountFrequency (%)
Import Quantity 724
16.1%
Food 705
15.7%
Domestic supply quantity 673
15.0%
Food supply quantity (kg/capita/yr) 635
14.2%
Export Quantity 612
13.6%
Residuals 595
13.3%
Production 533
11.9%
Total Population - Both sexes 7
 
0.2%

Length

2023-07-16T16:04:18.344499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:04:18.871499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
quantity 2644
29.1%
food 1340
14.7%
supply 1308
14.4%
import 724
 
8.0%
domestic 673
 
7.4%
kg/capita/yr 635
 
7.0%
export 612
 
6.7%
residuals 595
 
6.5%
production 533
 
5.9%
total 7
 
0.1%
Other values (4) 28
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 8486
 
11.8%
o 5783
 
8.0%
i 5087
 
7.1%
u 5087
 
7.1%
4615
 
6.4%
p 4594
 
6.4%
y 4587
 
6.4%
a 4523
 
6.3%
s 3185
 
4.4%
n 3184
 
4.4%
Other values (24) 22994
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59129
82.0%
Uppercase Letter 5834
 
8.1%
Space Separator 4615
 
6.4%
Other Punctuation 1270
 
1.8%
Open Punctuation 635
 
0.9%
Close Punctuation 635
 
0.9%
Dash Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 8486
14.4%
o 5783
9.8%
i 5087
8.6%
u 5087
8.6%
p 4594
 
7.8%
y 4587
 
7.8%
a 4523
 
7.6%
s 3185
 
5.4%
n 3184
 
5.4%
r 2504
 
4.2%
Other values (10) 12109
20.5%
Uppercase Letter
ValueCountFrequency (%)
F 1340
23.0%
Q 1336
22.9%
I 724
12.4%
D 673
11.5%
E 612
10.5%
R 595
10.2%
P 540
9.3%
T 7
 
0.1%
B 7
 
0.1%
Space Separator
ValueCountFrequency (%)
4615
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1270
100.0%
Open Punctuation
ValueCountFrequency (%)
( 635
100.0%
Close Punctuation
ValueCountFrequency (%)
) 635
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64963
90.1%
Common 7162
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8486
13.1%
o 5783
 
8.9%
i 5087
 
7.8%
u 5087
 
7.8%
p 4594
 
7.1%
y 4587
 
7.1%
a 4523
 
7.0%
s 3185
 
4.9%
n 3184
 
4.9%
r 2504
 
3.9%
Other values (19) 17943
27.6%
Common
ValueCountFrequency (%)
4615
64.4%
/ 1270
 
17.7%
( 635
 
8.9%
) 635
 
8.9%
- 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 8486
 
11.8%
o 5783
 
8.0%
i 5087
 
7.1%
u 5087
 
7.1%
4615
 
6.4%
p 4594
 
6.4%
y 4587
 
6.4%
a 4523
 
6.3%
s 3185
 
4.4%
n 3184
 
4.4%
Other values (24) 22994
31.9%
Distinct98
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2023-07-16T16:04:19.889500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters22420
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF2501
2nd rowF2511
3rd rowF2513
4th rowF2514
5th rowF2515
ValueCountFrequency (%)
f2761 68
 
1.5%
f2763 62
 
1.4%
f2764 58
 
1.3%
f2765 58
 
1.3%
f2762 58
 
1.3%
f2767 55
 
1.2%
f2775 54
 
1.2%
f2807 49
 
1.1%
f2549 49
 
1.1%
f2731 49
 
1.1%
Other values (88) 3924
87.5%
2023-07-16T16:04:20.583507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5168
23.1%
F 4484
20.0%
5 3174
14.2%
6 2262
10.1%
7 2052
 
9.2%
1 1350
 
6.0%
3 1261
 
5.6%
4 1016
 
4.5%
8 707
 
3.2%
0 617
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17936
80.0%
Uppercase Letter 4484
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5168
28.8%
5 3174
17.7%
6 2262
12.6%
7 2052
 
11.4%
1 1350
 
7.5%
3 1261
 
7.0%
4 1016
 
5.7%
8 707
 
3.9%
0 617
 
3.4%
9 329
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
F 4484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17936
80.0%
Latin 4484
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5168
28.8%
5 3174
17.7%
6 2262
12.6%
7 2052
 
11.4%
1 1350
 
7.5%
3 1261
 
7.0%
4 1016
 
5.7%
8 707
 
3.9%
0 617
 
3.4%
9 329
 
1.8%
Latin
ValueCountFrequency (%)
F 4484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5168
23.1%
F 4484
20.0%
5 3174
14.2%
6 2262
10.1%
7 2052
 
9.2%
1 1350
 
6.0%
3 1261
 
5.6%
4 1016
 
4.5%
8 707
 
3.2%
0 617
 
2.8%

Item
Text

Distinct98
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2023-07-16T16:04:21.024507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length31
Median length21
Mean length14.361285
Min length4

Characters and Unicode

Total characters64396
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPopulation
2nd rowWheat and products
3rd rowBarley and products
4th rowMaize and products
5th rowRye and products
ValueCountFrequency (%)
and 992
 
10.0%
products 903
 
9.1%
oil 660
 
6.7%
other 637
 
6.4%
fish 322
 
3.3%
meat 192
 
1.9%
sugar 143
 
1.4%
141
 
1.4%
potatoes 97
 
1.0%
beans 96
 
1.0%
Other values (113) 5711
57.7%
2023-07-16T16:04:22.157429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6253
 
9.7%
5410
 
8.4%
a 4769
 
7.4%
s 4446
 
6.9%
t 4107
 
6.4%
r 3674
 
5.7%
n 3508
 
5.4%
o 3376
 
5.2%
i 2879
 
4.5%
d 2858
 
4.4%
Other values (41) 23116
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50031
77.7%
Uppercase Letter 7230
 
11.2%
Space Separator 5410
 
8.4%
Other Punctuation 1204
 
1.9%
Close Punctuation 184
 
0.3%
Open Punctuation 184
 
0.3%
Dash Punctuation 153
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6253
12.5%
a 4769
9.5%
s 4446
 
8.9%
t 4107
 
8.2%
r 3674
 
7.3%
n 3508
 
7.0%
o 3376
 
6.7%
i 2879
 
5.8%
d 2858
 
5.7%
l 2750
 
5.5%
Other values (15) 11411
22.8%
Uppercase Letter
ValueCountFrequency (%)
O 1606
22.2%
M 720
10.0%
P 675
9.3%
C 642
 
8.9%
S 601
 
8.3%
F 566
 
7.8%
B 512
 
7.1%
R 354
 
4.9%
G 328
 
4.5%
A 299
 
4.1%
Other values (10) 927
12.8%
Other Punctuation
ValueCountFrequency (%)
, 1157
96.1%
& 47
 
3.9%
Space Separator
ValueCountFrequency (%)
5410
100.0%
Close Punctuation
ValueCountFrequency (%)
) 184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57261
88.9%
Common 7135
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6253
 
10.9%
a 4769
 
8.3%
s 4446
 
7.8%
t 4107
 
7.2%
r 3674
 
6.4%
n 3508
 
6.1%
o 3376
 
5.9%
i 2879
 
5.0%
d 2858
 
5.0%
l 2750
 
4.8%
Other values (35) 18641
32.6%
Common
ValueCountFrequency (%)
5410
75.8%
, 1157
 
16.2%
) 184
 
2.6%
( 184
 
2.6%
- 153
 
2.1%
& 47
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6253
 
9.7%
5410
 
8.4%
a 4769
 
7.4%
s 4446
 
6.9%
t 4107
 
6.4%
r 3674
 
5.7%
n 3508
 
5.4%
o 3376
 
5.2%
i 2879
 
4.5%
d 2858
 
4.4%
Other values (41) 23116
35.9%

Unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
1000 t
3842 
kg
635 
1000 No
 
7

Length

Max length7
Median length6
Mean length5.4351026
Min length2

Characters and Unicode

Total characters24371
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000 No
2nd rowkg
3rd rowkg
4th rowkg
5th rowkg

Common Values

ValueCountFrequency (%)
1000 t 3842
85.7%
kg 635
 
14.2%
1000 No 7
 
0.2%

Length

2023-07-16T16:04:22.419495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:04:22.682489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1000 3849
46.2%
t 3842
46.1%
kg 635
 
7.6%
no 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11547
47.4%
1 3849
 
15.8%
3849
 
15.8%
t 3842
 
15.8%
k 635
 
2.6%
g 635
 
2.6%
N 7
 
< 0.1%
o 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15396
63.2%
Lowercase Letter 5119
 
21.0%
Space Separator 3849
 
15.8%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3842
75.1%
k 635
 
12.4%
g 635
 
12.4%
o 7
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 11547
75.0%
1 3849
 
25.0%
Space Separator
ValueCountFrequency (%)
3849
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19245
79.0%
Latin 5126
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3842
75.0%
k 635
 
12.4%
g 635
 
12.4%
N 7
 
0.1%
o 7
 
0.1%
Common
ValueCountFrequency (%)
0 11547
60.0%
1 3849
 
20.0%
3849
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11547
47.4%
1 3849
 
15.8%
3849
 
15.8%
t 3842
 
15.8%
k 635
 
2.6%
g 635
 
2.6%
N 7
 
< 0.1%
o 7
 
< 0.1%

Flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
I
3628 
E
849 
X
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4484
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
X 7
 
0.2%

Length

2023-07-16T16:04:22.907160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:04:23.186166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
i 3628
80.9%
e 849
 
18.9%
x 7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
X 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4484
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
X 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4484
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
X 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
X 7
 
0.2%

Flag Description
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
Imputed value
3628 
Estimated value
849 
Figure from international organizations
 
7

Length

Max length39
Median length13
Mean length13.419269
Min length13

Characters and Unicode

Total characters60172
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFigure from international organizations
2nd rowEstimated value
3rd rowEstimated value
4th rowEstimated value
5th rowEstimated value

Common Values

ValueCountFrequency (%)
Imputed value 3628
80.9%
Estimated value 849
 
18.9%
Figure from international organizations 7
 
0.2%

Length

2023-07-16T16:04:23.435157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:04:23.704154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
value 4477
49.8%
imputed 3628
40.4%
estimated 849
 
9.5%
figure 7
 
0.1%
from 7
 
0.1%
international 7
 
0.1%
organizations 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 8968
14.9%
u 8112
13.5%
a 5354
8.9%
t 5347
8.9%
4498
7.5%
l 4484
7.5%
m 4484
7.5%
d 4477
7.4%
v 4477
7.4%
I 3628
6.0%
Other values (11) 6343
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51190
85.1%
Space Separator 4498
 
7.5%
Uppercase Letter 4484
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8968
17.5%
u 8112
15.8%
a 5354
10.5%
t 5347
10.4%
l 4484
8.8%
m 4484
8.8%
d 4477
8.7%
v 4477
8.7%
p 3628
7.1%
i 884
 
1.7%
Other values (7) 975
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
I 3628
80.9%
E 849
 
18.9%
F 7
 
0.2%
Space Separator
ValueCountFrequency (%)
4498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55674
92.5%
Common 4498
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8968
16.1%
u 8112
14.6%
a 5354
9.6%
t 5347
9.6%
l 4484
8.1%
m 4484
8.1%
d 4477
8.0%
v 4477
8.0%
I 3628
6.5%
p 3628
6.5%
Other values (10) 2715
 
4.9%
Common
ValueCountFrequency (%)
4498
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8968
14.9%
u 8112
13.5%
a 5354
8.9%
t 5347
8.9%
4498
7.5%
l 4484
7.5%
m 4484
7.5%
d 4477
7.4%
v 4477
7.4%
I 3628
6.0%
Other values (11) 6343
10.5%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct636
Distinct (%)20.4%
Missing1366
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean196.01382
Minimum-11
Maximum64563.85
Zeros1403
Zeros (%)31.3%
Negative22
Negative (%)0.5%
Memory size35.2 KiB
2023-07-16T16:04:23.979156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile0
Q10
median0.14
Q314
95-th percentile489.6
Maximum64563.85
Range64574.85
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1993.6384
Coefficient of variation (CV)10.170907
Kurtosis559.5933
Mean196.01382
Median Absolute Deviation (MAD)0.14
Skewness22.096821
Sum611171.08
Variance3974594
MonotonicityNot monotonic
2023-07-16T16:04:24.306159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1403
31.3%
1 121
 
2.7%
2 67
 
1.5%
3 55
 
1.2%
0.01 44
 
1.0%
4 41
 
0.9%
5 40
 
0.9%
6 28
 
0.6%
14 25
 
0.6%
8 25
 
0.6%
Other values (626) 1269
28.3%
(Missing) 1366
30.5%
ValueCountFrequency (%)
-11 1
 
< 0.1%
-7 1
 
< 0.1%
-4 2
 
< 0.1%
-3 3
 
0.1%
-2 3
 
0.1%
-1.51 1
 
< 0.1%
-1 10
 
0.2%
-0.03 1
 
< 0.1%
0 1403
31.3%
0.01 44
 
1.0%
ValueCountFrequency (%)
64563.85 1
< 0.1%
44347 1
< 0.1%
42031 1
< 0.1%
32428 1
< 0.1%
31269 2
< 0.1%
28695 1
< 0.1%
10039 1
< 0.1%
5710 2
< 0.1%
4880 1
< 0.1%
4849 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct659
Distinct (%)21.1%
Missing1367
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean201.33324
Minimum-12
Maximum66755.15
Zeros1371
Zeros (%)30.6%
Negative15
Negative (%)0.3%
Memory size35.2 KiB
2023-07-16T16:04:24.629155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile0
Q10
median0.21
Q314.77
95-th percentile524.4
Maximum66755.15
Range66767.15
Interquartile range (IQR)14.77

Descriptive statistics

Standard deviation2058.5295
Coefficient of variation (CV)10.224489
Kurtosis561.24974
Mean201.33324
Median Absolute Deviation (MAD)0.21
Skewness22.149118
Sum627555.72
Variance4237543.9
MonotonicityNot monotonic
2023-07-16T16:04:24.943160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1371
30.6%
1 128
 
2.9%
2 86
 
1.9%
4 54
 
1.2%
3 45
 
1.0%
0.01 35
 
0.8%
5 31
 
0.7%
6 28
 
0.6%
8 27
 
0.6%
9 27
 
0.6%
Other values (649) 1285
28.7%
(Missing) 1367
30.5%
ValueCountFrequency (%)
-12 1
 
< 0.1%
-10 1
 
< 0.1%
-6 2
 
< 0.1%
-5 1
 
< 0.1%
-3 1
 
< 0.1%
-2 2
 
< 0.1%
-1 6
 
0.1%
-0.31 1
 
< 0.1%
0 1371
30.6%
0.01 35
 
0.8%
ValueCountFrequency (%)
66755.15 1
< 0.1%
45674 1
< 0.1%
43178 1
< 0.1%
33477 1
< 0.1%
32539 2
< 0.1%
29860 1
< 0.1%
10293 1
< 0.1%
5307 2
< 0.1%
4964 1
< 0.1%
4924 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct663
Distinct (%)21.3%
Missing1367
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean207.85372
Minimum-14
Maximum69020.75
Zeros1348
Zeros (%)30.1%
Negative15
Negative (%)0.3%
Memory size35.2 KiB
2023-07-16T16:04:25.340154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile0
Q10
median0.33
Q315
95-th percentile539.4
Maximum69020.75
Range69034.75
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2115.9156
Coefficient of variation (CV)10.17983
Kurtosis567.88903
Mean207.85372
Median Absolute Deviation (MAD)0.33
Skewness22.250176
Sum647880.03
Variance4477098.7
MonotonicityNot monotonic
2023-07-16T16:04:25.660158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1348
30.1%
1 136
 
3.0%
2 77
 
1.7%
3 56
 
1.2%
0.01 43
 
1.0%
4 42
 
0.9%
5 33
 
0.7%
9 27
 
0.6%
14 25
 
0.6%
10 25
 
0.6%
Other values (653) 1305
29.1%
(Missing) 1367
30.5%
ValueCountFrequency (%)
-14 1
 
< 0.1%
-5 1
 
< 0.1%
-4 2
 
< 0.1%
-3 2
 
< 0.1%
-2 1
 
< 0.1%
-1 8
 
0.2%
0 1348
30.1%
0.01 43
 
1.0%
0.02 20
 
0.4%
0.03 14
 
0.3%
ValueCountFrequency (%)
69020.75 1
< 0.1%
47053 1
< 0.1%
44343 1
< 0.1%
34559 1
< 0.1%
33034 1
< 0.1%
33033 1
< 0.1%
30315 1
< 0.1%
10550 1
< 0.1%
5824 2
< 0.1%
5462 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct655
Distinct (%)21.0%
Missing1366
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean215.24276
Minimum-119
Maximum71358.81
Zeros1349
Zeros (%)30.1%
Negative31
Negative (%)0.7%
Memory size35.2 KiB
2023-07-16T16:04:25.984179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-119
5-th percentile0
Q10
median0.21
Q315
95-th percentile564.35
Maximum71358.81
Range71477.81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2177.9157
Coefficient of variation (CV)10.118415
Kurtosis572.30848
Mean215.24276
Median Absolute Deviation (MAD)0.21
Skewness22.301397
Sum671126.93
Variance4743316.7
MonotonicityNot monotonic
2023-07-16T16:04:26.287158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1349
30.1%
1 110
 
2.5%
2 83
 
1.9%
3 57
 
1.3%
4 49
 
1.1%
0.01 43
 
1.0%
9 29
 
0.6%
5 29
 
0.6%
6 28
 
0.6%
10 23
 
0.5%
Other values (645) 1318
29.4%
(Missing) 1366
30.5%
ValueCountFrequency (%)
-119 1
 
< 0.1%
-43 1
 
< 0.1%
-35 1
 
< 0.1%
-19 1
 
< 0.1%
-15 1
 
< 0.1%
-11 1
 
< 0.1%
-10 1
 
< 0.1%
-9 1
 
< 0.1%
-7 3
0.1%
-6 1
 
< 0.1%
ValueCountFrequency (%)
71358.81 1
< 0.1%
48483 1
< 0.1%
45520 1
< 0.1%
35695 1
< 0.1%
33923 1
< 0.1%
33918 1
< 0.1%
30319 1
< 0.1%
10812 1
< 0.1%
6674 2
< 0.1%
5356 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct725
Distinct (%)20.5%
Missing950
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean202.83733
Minimum-126
Maximum73767.45
Zeros1522
Zeros (%)33.9%
Negative34
Negative (%)0.8%
Memory size35.2 KiB
2023-07-16T16:04:26.604158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-126
5-th percentile0
Q10
median0.24
Q315
95-th percentile506
Maximum73767.45
Range73893.45
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2118.746
Coefficient of variation (CV)10.445542
Kurtosis642.13389
Mean202.83733
Median Absolute Deviation (MAD)0.24
Skewness23.567597
Sum716827.14
Variance4489084.6
MonotonicityNot monotonic
2023-07-16T16:04:26.984154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1522
33.9%
1 152
 
3.4%
2 92
 
2.1%
3 65
 
1.4%
5 54
 
1.2%
0.01 45
 
1.0%
4 44
 
1.0%
7 29
 
0.6%
6 28
 
0.6%
8 27
 
0.6%
Other values (715) 1476
32.9%
(Missing) 950
21.2%
ValueCountFrequency (%)
-126 1
< 0.1%
-85 1
< 0.1%
-56 1
< 0.1%
-41 1
< 0.1%
-40 1
< 0.1%
-29 1
< 0.1%
-24 1
< 0.1%
-21 1
< 0.1%
-11 1
< 0.1%
-8 1
< 0.1%
ValueCountFrequency (%)
73767.45 1
< 0.1%
49961 1
< 0.1%
46700 1
< 0.1%
36912 1
< 0.1%
35250 1
< 0.1%
34868 1
< 0.1%
31497 1
< 0.1%
11084 1
< 0.1%
9844.3 1
< 0.1%
6737 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct732
Distinct (%)20.7%
Missing955
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean207.89498
Minimum-70
Maximum76244.54
Zeros1531
Zeros (%)34.1%
Negative43
Negative (%)1.0%
Memory size35.2 KiB
2023-07-16T16:04:27.312155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-70
5-th percentile0
Q10
median0.18
Q315
95-th percentile515.716
Maximum76244.54
Range76314.54
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2176.4789
Coefficient of variation (CV)10.469126
Kurtosis650.18607
Mean207.89498
Median Absolute Deviation (MAD)0.18
Skewness23.666877
Sum733661.4
Variance4737060.3
MonotonicityNot monotonic
2023-07-16T16:04:27.628158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1531
34.1%
1 136
 
3.0%
2 93
 
2.1%
3 61
 
1.4%
5 59
 
1.3%
4 48
 
1.1%
0.01 47
 
1.0%
7 33
 
0.7%
10 28
 
0.6%
15 28
 
0.6%
Other values (722) 1465
32.7%
(Missing) 955
21.3%
ValueCountFrequency (%)
-70 1
 
< 0.1%
-31 1
 
< 0.1%
-30 1
 
< 0.1%
-28 1
 
< 0.1%
-26 3
0.1%
-23 1
 
< 0.1%
-18 1
 
< 0.1%
-17 1
 
< 0.1%
-14 2
< 0.1%
-8 2
< 0.1%
ValueCountFrequency (%)
76244.54 1
< 0.1%
51483 1
< 0.1%
47878 1
< 0.1%
38225 1
< 0.1%
35305 1
< 0.1%
34931 1
< 0.1%
31990 1
< 0.1%
11369 1
< 0.1%
10160.03 1
< 0.1%
7165 2
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct731
Distinct (%)20.6%
Missing943
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean208.21269
Minimum-109
Maximum78789.13
Zeros1530
Zeros (%)34.1%
Negative36
Negative (%)0.8%
Memory size35.2 KiB
2023-07-16T16:04:27.946159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-109
5-th percentile0
Q10
median0.26
Q314
95-th percentile529
Maximum78789.13
Range78898.13
Interquartile range (IQR)14

Descriptive statistics

Standard deviation2228.2379
Coefficient of variation (CV)10.701739
Kurtosis666.1233
Mean208.21269
Median Absolute Deviation (MAD)0.26
Skewness23.972868
Sum737281.15
Variance4965044.3
MonotonicityNot monotonic
2023-07-16T16:04:28.270160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1530
34.1%
1 151
 
3.4%
2 111
 
2.5%
5 65
 
1.4%
3 64
 
1.4%
0.01 45
 
1.0%
4 42
 
0.9%
10 35
 
0.8%
7 29
 
0.6%
11 27
 
0.6%
Other values (721) 1442
32.2%
(Missing) 943
21.0%
ValueCountFrequency (%)
-109 1
< 0.1%
-71 1
< 0.1%
-34 1
< 0.1%
-26 1
< 0.1%
-21 1
< 0.1%
-17 1
< 0.1%
-15 1
< 0.1%
-13 1
< 0.1%
-12 1
< 0.1%
-10 1
< 0.1%
ValueCountFrequency (%)
78789.13 1
< 0.1%
53049 1
< 0.1%
49052 1
< 0.1%
39649 1
< 0.1%
35501 1
< 0.1%
35500 1
< 0.1%
32670 1
< 0.1%
11669 1
< 0.1%
10488 1
< 0.1%
7095 2
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct731
Distinct (%)20.6%
Missing939
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean213.90506
Minimum-82
Maximum81398.76
Zeros1533
Zeros (%)34.2%
Negative45
Negative (%)1.0%
Memory size35.2 KiB
2023-07-16T16:04:28.605154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-82
5-th percentile0
Q10
median0.27
Q315
95-th percentile555.2
Maximum81398.76
Range81480.76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2305.716
Coefficient of variation (CV)10.779156
Kurtosis663.87625
Mean213.90506
Median Absolute Deviation (MAD)0.27
Skewness23.9918
Sum758293.45
Variance5316326.2
MonotonicityNot monotonic
2023-07-16T16:04:28.912157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1533
34.2%
1 145
 
3.2%
2 88
 
2.0%
3 70
 
1.6%
4 54
 
1.2%
5 51
 
1.1%
0.01 39
 
0.9%
8 34
 
0.8%
6 30
 
0.7%
9 28
 
0.6%
Other values (721) 1473
32.9%
(Missing) 939
20.9%
ValueCountFrequency (%)
-82 1
< 0.1%
-74 1
< 0.1%
-42 1
< 0.1%
-38 1
< 0.1%
-21 1
< 0.1%
-14 1
< 0.1%
-13 1
< 0.1%
-12 2
< 0.1%
-9 2
< 0.1%
-8 1
< 0.1%
ValueCountFrequency (%)
81398.76 1
< 0.1%
54660 1
< 0.1%
50221 1
< 0.1%
41167 1
< 0.1%
37701 1
< 0.1%
37700 1
< 0.1%
34597 1
< 0.1%
11981 1
< 0.1%
10827.02 1
< 0.1%
6681 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct742
Distinct (%)20.8%
Missing923
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean224.55801
Minimum-102
Maximum84068.09
Zeros1536
Zeros (%)34.3%
Negative47
Negative (%)1.0%
Memory size35.2 KiB
2023-07-16T16:04:29.249718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-102
5-th percentile0
Q10
median0.26
Q316
95-th percentile568
Maximum84068.09
Range84170.09
Interquartile range (IQR)16

Descriptive statistics

Standard deviation2381.1313
Coefficient of variation (CV)10.603636
Kurtosis658.11737
Mean224.55801
Median Absolute Deviation (MAD)0.26
Skewness23.809113
Sum799651.06
Variance5669786.3
MonotonicityNot monotonic
2023-07-16T16:04:29.557718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1536
34.3%
1 144
 
3.2%
2 90
 
2.0%
3 67
 
1.5%
5 48
 
1.1%
4 45
 
1.0%
0.01 42
 
0.9%
7 32
 
0.7%
6 32
 
0.7%
11 32
 
0.7%
Other values (732) 1493
33.3%
(Missing) 923
20.6%
ValueCountFrequency (%)
-102 1
< 0.1%
-58 1
< 0.1%
-28 1
< 0.1%
-24 1
< 0.1%
-20 1
< 0.1%
-18 2
< 0.1%
-17 1
< 0.1%
-15 1
< 0.1%
-14 1
< 0.1%
-12 1
< 0.1%
ValueCountFrequency (%)
84068.09 1
< 0.1%
56313 1
< 0.1%
51393 1
< 0.1%
42729 1
< 0.1%
38879 1
< 0.1%
38873 1
< 0.1%
35675 1
< 0.1%
12302 1
< 0.1%
11175.37 1
< 0.1%
8372 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct775
Distinct (%)19.1%
Missing418
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean208.51965
Minimum-187
Maximum86790.57
Zeros1813
Zeros (%)40.4%
Negative50
Negative (%)1.1%
Memory size35.2 KiB
2023-07-16T16:04:29.845714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-187
5-th percentile0
Q10
median0.1
Q313.6725
95-th percentile505.75
Maximum86790.57
Range86977.57
Interquartile range (IQR)13.6725

Descriptive statistics

Standard deviation2307.3048
Coefficient of variation (CV)11.065167
Kurtosis740.81473
Mean208.51965
Median Absolute Deviation (MAD)0.13
Skewness25.182545
Sum847840.9
Variance5323655.3
MonotonicityNot monotonic
2023-07-16T16:04:30.140718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1813
40.4%
1 167
 
3.7%
2 96
 
2.1%
3 76
 
1.7%
4 60
 
1.3%
0.01 58
 
1.3%
5 56
 
1.2%
10 42
 
0.9%
9 34
 
0.8%
6 31
 
0.7%
Other values (765) 1633
36.4%
(Missing) 418
 
9.3%
ValueCountFrequency (%)
-187 1
< 0.1%
-22 1
< 0.1%
-19 1
< 0.1%
-18 1
< 0.1%
-17 1
< 0.1%
-12 1
< 0.1%
-11 1
< 0.1%
-10 1
< 0.1%
-8 2
< 0.1%
-7 1
< 0.1%
ValueCountFrequency (%)
86790.57 1
< 0.1%
58005.46 1
< 0.1%
52573.97 1
< 0.1%
44269.59 1
< 0.1%
40073 1
< 0.1%
40050 1
< 0.1%
36778 1
< 0.1%
12626.95 1
< 0.1%
11530.58 1
< 0.1%
11062.11 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct787
Distinct (%)19.6%
Missing473
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean219.08283
Minimum-201
Maximum89561.4
Zeros1789
Zeros (%)39.9%
Negative52
Negative (%)1.2%
Memory size35.2 KiB
2023-07-16T16:04:30.440720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-201
5-th percentile0
Q10
median0.1
Q314.87
95-th percentile536
Maximum89561.4
Range89762.4
Interquartile range (IQR)14.87

Descriptive statistics

Standard deviation2392.8606
Coefficient of variation (CV)10.922173
Kurtosis731.30471
Mean219.08283
Median Absolute Deviation (MAD)0.14
Skewness24.976405
Sum878741.25
Variance5725781.6
MonotonicityNot monotonic
2023-07-16T16:04:30.770716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1789
39.9%
1 149
 
3.3%
2 84
 
1.9%
3 75
 
1.7%
4 62
 
1.4%
0.01 59
 
1.3%
5 48
 
1.1%
7 36
 
0.8%
9 32
 
0.7%
13 30
 
0.7%
Other values (777) 1647
36.7%
(Missing) 473
 
10.5%
ValueCountFrequency (%)
-201 1
< 0.1%
-50 1
< 0.1%
-44 1
< 0.1%
-30 1
< 0.1%
-29 1
< 0.1%
-22 1
< 0.1%
-20 1
< 0.1%
-16 2
< 0.1%
-15 2
< 0.1%
-13 1
< 0.1%
ValueCountFrequency (%)
89561.4 1
< 0.1%
59734.22 1
< 0.1%
53771.3 1
< 0.1%
45741.01 1
< 0.1%
41016 1
< 0.1%
41014 1
< 0.1%
37697 1
< 0.1%
12952.22 1
< 0.1%
11890.78 1
< 0.1%
11193.73 1
< 0.1%

Interactions

2023-07-16T16:04:08.037758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:12.530849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:18.875911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:24.798907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:29.886908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:33.776685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:37.863693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:42.288438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:46.536441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:50.974081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:55.235078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:59.462080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:04.178076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:08.433771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:13.896384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:19.553912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:25.462911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:30.212907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:34.102691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:38.173691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:42.593435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:46.843440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:51.291081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:55.580078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:59.746082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:04.479085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:08.709760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:14.423906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:19.987911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:25.996912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:30.500910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:34.387589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:38.453690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:42.894435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:47.259441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:51.584080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:55.869083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:00.029078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:04.722083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:09.283501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:14.762908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:20.382906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:26.333909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:30.795910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:34.688578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:38.889691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:43.236443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:47.663436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:51.872084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:56.164079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:00.335083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:04.972077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:09.731503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:15.270907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:20.809913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:26.640906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:31.098909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:35.036581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:39.326434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:43.794437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:48.011439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:52.200077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:56.467084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:00.612078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:05.232762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:10.115499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:15.801908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:21.167908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:26.936910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:31.402969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:35.377662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:39.632438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:44.097446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:48.534436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:52.495079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:56.753084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:00.934079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:05.492761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:10.485496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:16.395909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:21.808912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:27.238909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:31.704337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:35.712712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:39.945435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:44.392441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:48.853449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:52.796083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:57.069077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:01.322078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:05.744757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:10.750498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:16.811906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:22.357906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:27.531909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:32.002649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:36.038702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:40.253452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:44.703438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:49.168440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:53.098085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:57.380089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:01.698083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:06.075774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:11.041496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:17.136909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:22.753906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:27.831909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:32.318742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:36.394719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:40.563437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:45.005441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:49.512439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:53.439078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:58.017080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:02.088079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:06.385759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:11.459511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:17.541913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:23.058910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:28.241910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:32.633759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:36.712701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:40.871439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:45.336439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:49.833079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:53.747077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:58.338079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:02.554085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:06.668760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:11.772499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:17.928906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:23.517905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:28.761909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:32.943764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:37.016649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:41.180441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:45.666436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:50.152078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:54.133081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:58.657077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:03.014084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:07.177761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:12.057505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:18.207912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:23.928915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:29.062906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:33.232763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:37.282691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:41.450439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:46.002437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:50.432078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:54.419080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:58.916081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:03.466087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:07.434762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:12.410497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:18.594912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:24.376909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:29.624907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:33.513720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:37.539724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:41.994439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:46.274437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:50.692080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:54.815082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:03:59.188079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:03.837085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T16:04:07.679760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-16T16:04:31.027716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Area Code (M49)Element Code20102011201220132014201520162017201820192020AreaElementUnitFlagFlag Description
Area Code (M49)1.0000.0150.0030.0050.0150.0110.0930.0850.0860.0790.0820.0590.0571.0000.0370.0000.0000.000
Element Code0.0151.000-0.082-0.080-0.062-0.059-0.023-0.040-0.035-0.022-0.021-0.035-0.0240.0420.9990.7070.5960.596
20100.003-0.0821.0000.9640.9510.9460.9260.9170.9170.9110.9050.9050.9090.0170.4450.7060.7060.706
20110.005-0.0800.9641.0000.9650.9490.9300.9240.9220.9200.9120.9100.9120.0170.4450.7060.7060.706
20120.015-0.0620.9510.9651.0000.9550.9370.9310.9300.9220.9180.9180.9160.0170.4450.7060.7060.706
20130.011-0.0590.9460.9490.9551.0000.9440.9260.9260.9290.9200.9220.9180.0170.4450.7060.7060.706
20140.093-0.0230.9260.9300.9370.9441.0000.9560.9520.9420.9320.9320.9310.0220.4460.7060.7060.706
20150.085-0.0400.9170.9240.9310.9260.9561.0000.9650.9550.9490.9400.9350.0220.4460.7060.7060.706
20160.086-0.0350.9170.9220.9300.9260.9520.9651.0000.9660.9480.9420.9380.0220.4460.7060.7060.706
20170.079-0.0220.9110.9200.9220.9290.9420.9550.9661.0000.9640.9500.9470.0220.4460.7060.7060.706
20180.082-0.0210.9050.9120.9180.9200.9320.9490.9480.9641.0000.9580.9490.0200.4200.6660.6660.666
20190.059-0.0350.9050.9100.9180.9220.9320.9400.9420.9500.9581.0000.9630.0240.3950.6260.6260.626
20200.057-0.0240.9090.9120.9160.9180.9310.9350.9380.9470.9490.9631.0000.0250.3950.6260.6260.626
Area1.0000.0420.0170.0170.0170.0170.0220.0220.0220.0220.0200.0240.0251.0000.0240.0000.0000.000
Element0.0370.9990.4450.4450.4450.4450.4460.4460.4460.4460.4200.3950.3950.0241.0000.9990.9260.926
Unit0.0000.7070.7060.7060.7060.7060.7060.7060.7060.7060.6660.6260.6260.0000.9991.0000.9240.924
Flag0.0000.5960.7060.7060.7060.7060.7060.7060.7060.7060.6660.6260.6260.0000.9260.9241.0001.000
Flag Description0.0000.5960.7060.7060.7060.7060.7060.7060.7060.7060.6660.6260.6260.0000.9260.9241.0001.000

Missing values

2023-07-16T16:04:12.811496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T16:04:13.810497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-16T16:04:14.465497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearArea Code (M49)AreaElement CodeElementItem Code (CPC)ItemUnitFlagFlag Description20102011201220132014201520162017201820192020
0108Burundi511Total Population - Both sexesF2501Population1000 NoXFigure from international organizationsNaNNaNNaNNaN9844.3010160.0310488.0010827.0211175.3711530.5811890.78
1108Burundi645Food supply quantity (kg/capita/yr)F2511Wheat and productskgEEstimated valueNaNNaNNaNNaN4.742.485.266.417.027.635.63
2108Burundi645Food supply quantity (kg/capita/yr)F2513Barley and productskgEEstimated valueNaNNaNNaNNaN0.000.000.000.000.000.000.00
3108Burundi645Food supply quantity (kg/capita/yr)F2514Maize and productskgEEstimated valueNaNNaNNaNNaN13.5716.0823.6623.6728.4923.9221.54
4108Burundi645Food supply quantity (kg/capita/yr)F2515Rye and productskgEEstimated valueNaNNaNNaNNaNNaNNaNNaN0.000.00NaNNaN
5108Burundi645Food supply quantity (kg/capita/yr)F2516OatskgEEstimated valueNaNNaNNaNNaN0.000.000.000.000.000.000.00
6108Burundi645Food supply quantity (kg/capita/yr)F2517Millet and productskgEEstimated valueNaNNaNNaNNaN0.200.190.190.180.180.170.31
7108Burundi645Food supply quantity (kg/capita/yr)F2518Sorghum and productskgEEstimated valueNaNNaNNaNNaN0.000.190.240.120.270.000.57
8108Burundi645Food supply quantity (kg/capita/yr)F2520Cereals, OtherkgEEstimated valueNaNNaNNaNNaN0.531.402.562.592.110.961.04
9108Burundi645Food supply quantity (kg/capita/yr)F2531Potatoes and productskgEEstimated valueNaNNaNNaNNaN5.736.076.795.555.3713.8814.41
YearArea Code (M49)AreaElement CodeElementItem Code (CPC)ItemUnitFlagFlag Description20102011201220132014201520162017201820192020
4474834United Republic of Tanzania5911Export QuantityF2769Aquatic Animals, Others1000 tIImputed value0.000.000.00.000.000.000.000.000.000.00NaN
4475834United Republic of Tanzania5911Export QuantityF2775Aquatic Plants1000 tEEstimated valueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.55
4476834United Republic of Tanzania5911Export QuantityF2775Aquatic Plants1000 tIImputed value12.1814.7710.011.778.409.3510.2811.5511.5511.55NaN
4477834United Republic of Tanzania5911Export QuantityF2781Fish, Body Oil1000 tEEstimated valueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00
4478834United Republic of Tanzania5911Export QuantityF2781Fish, Body Oil1000 tIImputed value0.000.000.00.000.020.000.000.000.000.00NaN
4479834United Republic of Tanzania5911Export QuantityF2782Fish, Liver Oil1000 tEEstimated valueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00
4480834United Republic of Tanzania5911Export QuantityF2782Fish, Liver Oil1000 tIImputed value0.000.000.00.000.000.000.000.000.000.00NaN
4481834United Republic of Tanzania5911Export QuantityF2807Rice and products1000 tIImputed value75.0054.0027.079.00107.0023.0019.001.0046.00171.00527.00
4482834United Republic of Tanzania5911Export QuantityF2848Milk - Excluding Butter1000 tIImputed value0.000.000.00.000.000.000.000.000.000.000.00
4483834United Republic of Tanzania5911Export QuantityF2899Miscellaneous1000 tIImputed value3.001.001.00.000.000.000.001.000.000.000.00